基于容积卡尔曼滤波的辅助粒子滤波算法

Auxiliary Particle Filter Based on Cubature Kalman Filter

针对非线性系统的状态估计精度较低的问题,提出基于容积卡尔曼滤波(CKF)的辅助粒子滤波(APF)算法—CAPF算法。该算法采用容积数值积分原则计算非线性函数的均值和方差,生成粒子滤波算法的重要性密度函数,获得所需的带权值粒子,进而计算粒子均值,获得系统状态的最小均方误差估计。CAPF算法由于使用最新的量测信息产生粒子,因而提高了对系统状态估计的逼近程度。仿真结果表明,CAPF算法具有更高的滤波精度,验证了算法的可行性和有效性。

A new particle filter of APF algorithm based CKF（CAPF）was proposed to improve the low state estimation accuracy for nonlinear systems.The algorithm used the cubature rule based numerical integration method to calculate the mean and covariance,to generate the important density function for the particle filter,and to obtain the required particles with weights.Then the minimum square error state estimation is obtained.CAPF algorithm generates particles using the latest measurements so that the approximation to the system state.Simulation results showed that the algorithm was of higher accuracy of CAPF,and also verified the feasibility and effectiveness of the algorithm.